Know Your Surroundings: Exploiting Scene Information for Object Tracking (2003.11014v2)

Abstract: Current state-of-the-art trackers only rely on a target appearance model in order to localize the object in each frame. Such approaches are however prone to fail in case of e.g. fast appearance changes or presence of distractor objects, where a target appearance model alone is insufficient for robust tracking. Having the knowledge about the presence and locations of other objects in the surrounding scene can be highly beneficial in such cases. This scene information can be propagated through the sequence and used to, for instance, explicitly avoid distractor objects and eliminate target candidate regions. In this work, we propose a novel tracking architecture which can utilize scene information for tracking. Our tracker represents such information as dense localized state vectors, which can encode, for example, if the local region is target, background, or distractor. These state vectors are propagated through the sequence and combined with the appearance model output to localize the target. Our network is learned to effectively utilize the scene information by directly maximizing tracking performance on video segments. The proposed approach sets a new state-of-the-art on 3 tracking benchmarks, achieving an AO score of 63.6% on the recent GOT-10k dataset.

• The paper introduces a novel framework that leverages scene-aware state vectors to enhance object tracking beyond traditional appearance methods.
• The approach employs dense correspondence and RNN-based updates to fuse scene context with appearance cues for reliable target localization.
• Experimental results show a 63.6% average overlap on GOT-10k, outperforming previous models by 2.5% and validating the method's effectiveness.

Overview of "Know Your Surroundings: Exploiting Scene Information for Object Tracking"

This paper introduces an advanced object tracking framework designed to capitalize on scene context for target localization, which is a significant departure from previous methods that primarily utilize a target-centered appearance model. The authors, Goutam Bhat, Martin Danelljan, Luc Van Gool, and Radu Timofte, propose a methodology that extends beyond the conventional reliance on target appearance by integrating scene knowledge, represented as dense localized state vectors. This approach aims to enhance tracking robustness in situations where target appearance alone may prove inadequate, such as in the presence of distractor objects or during rapid appearance changes.

Methodology

The proposed architecture consists of several key components:

1. Scene-aware State Vectors: The core concept of the architecture is the use of state vectors that represent local regions within the video frames. These vectors inherently encapsulate context, identifying whether regions correspond to the target, background, or distractors.
2. State Propagation and Update: The state vectors are efficiently propagated through the video sequence using dense correspondence maps between frames. A recurrent neural network updates the state vectors using new information from each frame.
3. Integration with Appearance Model: The scene information integrated from the state vectors is effectively fused with the output of a powerful appearance model. This combination allows the system to produce more reliable target localization predictions.
4. Training on Video Segments: The entire network is trained end-to-end on video segments, maximizing tracking performance by leveraging diverse environmental cues and interactions.

Experimental Results

The authors provide comprehensive evaluations across several prominent benchmarks, including VOT-2018, GOT-10k, TrackingNet, OTB-100, and NFS. On the GOT-10k dataset, the proposed method achieves an average overlap score (AO) of 63.6%, surpassing previous approaches by a notable 2.5%. Such results underscore the effectiveness of incorporating scene context into the tracking process, particularly when tested against distractors and situations where traditional appearance models falter.

Implications and Future Directions

The research outlined suggests several implications for the field of object tracking and broader AI applications:

• Enhanced Robustness in Diverse Environments: By leveraging scene context, trackers can achieve improved performance in dynamic environments with complex interactions between multiple objects.
• Potential for Integration Across Vision Tasks: The approach could be adapted for use in related computer vision tasks such as object detection and activity recognition, where understanding the surrounding context can be equally critical.
• Development of More Holistic Models: The idea of incorporating scene information advocates for creating more comprehensive models that do not solely rely on target-centric data but consider the entirety of the visual field.

In the future, further exploration might involve refining the concept of state vectors, potentially adopting more sophisticated propagation techniques, or integrating additional modalities such as depth information. Such enhancements might provide even greater insight into the complex dynamics within video sequences, pushing the boundaries of what is achievable in real-time object tracking.